Quantification of <i>Cylindrocarpon destructans</i> f. sp. <i>panacis</i> in soils by real‐time PCR
Bibliographic record
Abstract
Ginseng ( Panax quinquefolius ) is an important cash crop in various regions of North America, but yields are often reduced by various root pathogens. A quantitative real‐time PCR (qPCR) assay for Cylindrocarpon destructans f. sp. panacis (CDP), the cause of a root rot and replant disease which discourages successive cropping of ginseng on the same site, was developed to quantify the levels of this pathogen in soils previously cropped with ginseng. DNA was extracted from 5‐g samples of soil. In pasteurized soils which were re‐infested with varying levels of the pathogen, qPCR estimates of pathogen DNA were significantly correlated with disease severity ( r = 0·494) and with counts of colony‐forming units ( r = 0·620) obtained with an agar medium. In several naturally infested field soils, qPCR estimates of CDP‐DNA concentration were significantly correlated with disease severity ( r = 0·765) and these concentrations were estimated to range from 0 to 1·48 ng g −1 dried soil. A principal components analysis did not show any strong relationships between soil chemistry factors and the concentration of pathogen DNA. The approach outlined here allows the quantification of current populations of CDP in soil many years after ginseng cultivation and the prediction of disease severity in future crops. The method should be generally applicable to root diseases of many crops.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".